Vittorio Solina is a Researcher at the Department of Mechanical, Energy and Management Engineering (DIMEG) of the University of Calabria, specializing in industrial systems simulation and digital twin technologies. His work spans Industry 4.0/5.0 applications, supply chain optimization, and human-robot collaboration. Affiliation: University of Calabria Academic Role: Researcher (Legge 240/10) Research Focus: Digital twins, simulation modeling, sustainable manufacturing His research emphasizes advanced simulation tools for decision support, including applications in Industry 4.0 , Smart Manufacturing , and Agri-Food Supply Chains . Recent work explores adaptive simulation optimization for Industry 5.0 and 5G-enabled robotics. Key publication trends include: (1) Digital twin integration for supply chain resilience (2024-2025), (2) Simulation automation methodologies (2025), and (3) Human-robot collaboration frameworks (2024).
Dr. Balaraman Ravindran is a Professor and Head of the Department of Data Science and Artificial Intelligence (DSAI) at IIT Madras. He also leads the Robert Bosch Centre for Data Science & Artificial Intelligence (RBCDSAI) and the Centre for Responsible AI (CeRAI). His research focuses on reinforcement learning, geometric deep learning, and ethical AI deployment. Education includes a PhD from the University of Massachusetts Amherst (2004) and MSc from the Indian Institute of Science, Bangalore (1996). He holds prestigious fellowships from AAAI and INAE, and is an ACM Distinguished Member. Key contributions include work on class imbalance learning (e.g., TODUS algorithm) and applications in healthcare, transportation, and social networks. He has advised over 20 students and secured grants from Google, TCS Research, and others. Labs/Teams: Heads RBCDSAI and CeRAI, collaborates with TCS Research and Google.
Andrea Arpaci-Dusseau is the Susan Beth Horwitz WARF Professor of Computer Sciences and Catherine A. Erickson Professor in the School of Computer, Data & Information Sciences at the University of Wisconsin-Madison. She has been a professor at UW-Madison since January 2000 and currently serves as Graduate Advising Chair for the Computer Sciences Department. Her research focuses on computer systems with primary emphasis on file and storage systems, but also making significant contributions in distributed systems, virtualization, and scheduling. According to csrankings.org, she has published the fourth-most papers in premier systems conferences (SOSP and OSDI) and the most at the top file and storage conference (FAST). Professor Arpaci-Dusseau's recent publications demonstrate continued leadership in storage systems research, with increasing focus on cloud architectures, persistent memory technologies, and system reliability. Her work bridges theoretical foundations with practical implementations, consistently addressing real-world challenges in modern computing environments. ACM SIGOPS Mark Weiser Award 2018 (highest honor in systems field) UW-Madison Van Hise Outreach Teaching Award 2017 Multiple Best Paper awards at FAST, SOSP, OSDI, and EuroSys conferences Carolyn Rosner Award for Excellence in Teaching (2010, 2017) Co-chaired major conferences including USENIX ATC '04, FAST '07, OSDI'18, and SOSP'24 Professor Arpaci-Dusseau has co-advised 28 Ph.D. students and is deeply committed to educational outreach through the CaTaPuLT project, which connects UW-Madison students with K-12 schools to teach computational thinking using Scratch. This initiative has reached hundreds of students across Madison and earned her significant recognition for teaching excellence. She leads the Arpaci-Dusseau Systems Lab (ADSL) and is involved with the Wisconsin Institute on Software-defined Datacenters in Madison (WISDoM), where her research group continues to advance state-of-the-art solutions in computer systems.
Sunita Sarawagi is a Professor at Computer Science and Engineering , IIT Bombay, and a member of AI Labs@CSE . She is also associated with the Center for Machine Intelligence and Data Science (CMInDS), which she founded in 2020. Education: PhD in Computer Science from UC Berkeley (Thesis: Query Processing in Tertiary Memory Databases), BTech in Computer Science from IIT Kharagpur Research Interests span machine learning , data analytics , graphical models , and structured learning , with applications in text segmentation, sequence modeling, domain adaptation, and human-in-the-loop systems. Her publications reveal a strong focus on integrating data mining with database systems , temporal data analysis , and information extraction using probabilistic methods. Professional Activities include serving on the IEEE John Von Neumann Medal committee (2017-), VLDB 2011 Research Track Co-chair , and multiple program committee roles at top conferences like ICML, KDD, and SIGMOD. Labs & Teams : Leads the SS Lab , a research group focused on probabilistic graphical models, sequence modeling, and data integration techniques.
Gierad Laput is a Senior Engineering Manager at Apple, leading the Perception and Interaction Engineering organization under Machine Intelligence Neural Design (MIND) at Apple AI/ML (AIML). He is also an Adjunct Professor at Carnegie Mellon University's School of Computer Science and Human-Computer Interaction Institute. His research sits at the intersection of sensing systems, interactive technologies, and machine learning, with applications in ambient intelligence, wearables, and AI-driven interactive experiences. Ph.D. in Computer Science from Carnegie Mellon University Founding member of CMU's Future Interfaces Group Former Google Ph.D. Fellow, Disney Imagineer, and recipient of 10 Best Paper Awards/Nominations His research agenda at Apple leverages cutting-edge hardware to develop multimodal ML systems for vision, audio, and motion sensors, enabling features like head gesture detection for AirPods, Apple Intelligence integrations, and health monitoring tools. Recent collaborative projects include Vision Pro hand interaction systems and WatchOS updates for cycling/fall detection. Key research areas include human-computer interaction , machine learning for sensor fusion , and smart environment sensing . Over 35 publications cover transformative technologies like ViBand (bio-acoustic sensing), EM-Sense (touch recognition), SkinTrack (arm as touchpad), and ElectroDermis (stretchable electronics). Scientific honors include: Fast Company Innovation by Design Award (2017) Google PhD Fellowship (2018) Swartz Entrepreneurial Fellowship (2018) Best Paper Awards at ACM UIST (2016) and ACM CHI (2015) As an educator, he has taught courses at CMU and the University of Michigan on on-device machine learning , signal processing , and human-centered design . His lab has mentored numerous researchers now pursuing PhDs at MIT, UW, CMU, and Northwestern.
Dr. Corey T. Callaghan is an Assistant Professor in the Department of Wildlife Ecology and Conservation at the University of Florida . Based at the Fort Lauderdale Research and Education Center , his research focuses on global change ecology using big data from citizen science platforms like eBird and iNaturalist, combined with geospatial analyses and macroecological theory . Ph.D. (2019) from UNSW Sydney M.S. (2015) from Florida Atlantic University B.S. + B.S. (2013) from Canisius College His work examines urban ecology , including how species traits influence urban tolerance across birds, amphibians, and butterflies. He develops adaptive sampling frameworks to optimize citizen science data collection while addressing biases. Current projects analyze human-nature interactions through citizen science participation, secondary data in photographs, and machine learning for biodiversity monitoring. He actively contributes to conservation strategies for urban biodiversity and data-driven restoration efforts . Recent publications highlight his expertise in cross-taxa comparisons , continental-scale analyses , and ecosystem service quantification . He leads the Global Ecology Research Group at the University of Florida, emphasizing collaborative science and open-source methodologies .
Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, affiliated with multiple research centers including the Berkeley Artificial Intelligence Research Lab (BAIR) and the Berkeley Center for Responsible, Decentralized Intelligence (RDI). She holds leadership roles in AI safety, cybersecurity, and blockchain research. Her work bridges machine learning, security, and distributed systems, with notable contributions to formal verification, privacy-preserving technologies, and AI governance. Education: Ph.D. (2002) and M.S. (1999) in Computer Science from UC Berkeley and Carnegie Mellon University, respectively. She previously served as an Assistant Professor at Carnegie Mellon University before joining UC Berkeley in 2007. Research Interests: Dawn's work focuses on AI safety, cybersecurity, and the ethical implications of frontier AI. She explores topics such as large language model vulnerabilities, zero-knowledge proofs, blockchain security, and policy frameworks for AI governance. Her research combines theoretical rigor with practical applications, addressing challenges in secure systems, adversarial machine learning, and privacy-preserving computation. Publications: Her recent work includes groundbreaking studies on LLM memorization, smart contract decompilation, and AI agent cybersecurity evaluation. She also leads initiatives like the Singapore Consensus and California Report on AI safety priorities, emphasizing global collaboration in risk mitigation. Notable Awards: MacArthur Fellowship (2010), ACM Fellow (2019), IEEE Fellow (2019) Advising & Grants: She advises projects on AI policy and cybersecurity, securing grants from NSF, DARPA, and industry collaborations. Her lab develops tools like CyberGym for evaluating AI agents and zkPyTorch for secure machine learning. Labs & Teams: Her research groups at UC Berkeley focus on cutting-edge projects in AI safety, blockchain, and cybersecurity, collaborating with industry leaders and policymakers to advance both technical and ethical standards.
Tianqi Chen is an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University (CMU), with a courtesy appointment as a Professor in the Electrical and Computer Engineering Department within the College of Engineering. His research focuses on scalable machine learning systems, compiler optimization, and efficient deep learning frameworks. He holds a PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Key contributions include the creation of XGBoost, Apache TVM, and MLC-LLM—widely adopted systems for machine learning and large language models. His work bridges algorithmic innovation with high-performance computing, emphasizing efficient deployment, quantization, and edge computing. Recent publications highlight advancements in LLM serving (e.g., WebLLM, Flashinfer), compiler-driven optimizations (e.g., TVM, Relax), and low-latency inference techniques (e.g., Magicdec, Tilus). These efforts address scalability, energy efficiency, and cross-platform compatibility in modern AI systems. Chen’s research has been applied to diverse domains, including music AI, browser-based inference, and microservice architectures for LLMs. His work underscores the importance of system-level thinking in advancing AI capabilities.
Claire Bowern is Professor of Linguistics at Yale University specializing in historical linguistics, language documentation, and Australian Indigenous languages. Her research employs computational phylogenetics to study language evolution and supports language revitalization through digital archives and fieldwork methodologies. Recent publications address: Phylogenetic signal in lexical evolution across language families Digital infrastructure for endangered language documentation (FLEx software analysis) Decolonizing linguistics pedagogy and research practices Her work consistently integrates linguistic, anthropological, and computational approaches to analyze language diversity and change. She contributes to global databases including Grambank and D-PLACE, examining links between linguistic, cultural, and environmental patterns.
Ruben Juanes is a Professor of Civil and Environmental Engineering and Earth, Atmospheric, and Planetary Sciences at MIT. His research focuses on multiphase flow in porous media, energy resources, and CO₂ sequestration. He holds appointments in both departments and has a strong interdisciplinary focus on geosciences and environmental engineering. His work bridges theory, simulation, and experimentation to address energy and environmental challenges. Education: Ingeniero de Caminos (Civil Engineering), University of La Coruña, Spain (1997) MS in Civil Engineering, UC Berkeley (1999) PhD in Civil Engineering, UC Berkeley (2003) Research interests emphasize fluid dynamics in geologic media, especially CO₂ storage, methane hydrates, and ecohydrology. His group develops computational models to predict large-scale Earth processes, with applications to carbon capture and storage, energy resource management, and subsurface engineering. Notable contributions include advancing understanding of fluid displacement mechanisms, capillary trapping in aquifers, and induced seismicity risks during CO₂ injection. His work has been recognized through awards like the APS Fellowship (2024), DOE Early Career Award (2010), and ARCO Energy Professorship (2008). Advising and Grants: Juanes advises graduate students in CEE and EAPS, focusing on thesis research in multiphase flow and geomechanics. His grants include NSF and DOE funding for projects on subsurface energy systems and induced seismicity. His lab, the Juanes Research Group, collaborates on experimental facilities like the FluidFlower CO₂ storage simulator. Labs/Teams: Active in MIT's Carbon Capture, Utilization, and Storage (CCUS) initiatives and the MIT Energy Initiative (MITEI). Collaborates with industry partners on field-scale CO₂ storage validation and subsurface monitoring technologies.
Prof. Konrad Schindler holds the position of Full Professor at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. He is also the Head of the Institute of Geodesy and Photogrammetry (IGP), leading research and educational activities in geomatics and computer vision. His career spans roles as a Photogrammetric Engineer, scientific assistant, postdoc researcher, and academic faculty across institutions including Graz University of Technology, Monash University, and TU Darmstadt before joining ETH Zürich in 2010. Education: Undergraduate studies in Geodesy (1992–1995), Graz University of Technology, Austria MEng in Photogrammetry and Geoinformation (1995–1999), Vienna University of Technology, Austria PhD in Computer Science (2001–2003), Graz University of Technology, Austria Research focuses on Photogrammetry , Remote Sensing , Computer Vision , and Image Understanding with interdisciplinary applications in environmental monitoring, geospatial analysis, and disaster response. He develops computational methods for 3D reconstruction, fusion of multi-modal data, and AI-driven solutions for satellite imagery interpretation. His work bridges geomatic engineering and machine learning to address challenges in urban mapping, climate modeling, and biological systems analysis. Publications reflect expertise in geospatial AI, diffusion models, and benchmarking datasets for disaster resilience. Notable works include Marigold (image analysis adaptation) and BRIGHT (building damage assessment). His research emphasizes practicality and scalability, such as affordable depth estimation and global biomass datasets. He has received the 2013 Marr Prize Honourable Mention (IEEE) and the 2012 U.V. Helava Award (ISPRS), alongside several Best Presentation Awards. His contributions span technical leadership, editorial roles (ISPRS Journal), and service to Swiss remote sensing commissions. Advising and grants: While no specific advisee names or grant details are listed, his career trajectory includes mentoring postdocs and junior faculty. He teaches advanced courses in Photogrammetry , Image Interpretation , and Machine Vision , integrating cutting-edge AI techniques into curricula. His research group collaborates on global-scale projects like canopy height mapping and satellite-based climate variable assessments. Labs/Teams: As Institute Head, he oversees the IGP lab at ETH Zürich, with prior affiliations including the Digital Perception Lab (Monash University) and the Computer Vision Lab (ETH Zurich). His work often involves multi-institutional collaborations focused on geospatial AI and environmental science.
Dr. Diyi Yang is an Assistant Professor in the Computer Science Department at Stanford University. She leads the Social and Language Technologies (SALT) Lab, affiliated with the Stanford NLP Group, Stanford HCI Group, Stanford AI Lab (SAIL), and Stanford Human-Centered Artificial Intelligence (HAI). Her research focuses on socially aware natural language processing, large language models (LLMs), and human-AI interaction, aiming to improve human-human and human-computer communication through socially grounded AI systems. Education: Ph.D. in Language Technologies Institute, Carnegie Mellon University (2013–2019) B.S. in ACM Honored Class, Shanghai Jiao Tong University (2009–2013) Research Interests: Dr. Yang’s work bridges computational social science and NLP. She explores how AI can understand social contexts in language use and develop systems that respect cultural norms, ethical standards, and human values. Her lab’s projects include AI companions for skill training (e.g., Rehearsal Dialects), norm-aware LLMs (NormBank), and frameworks for human-AI collaboration (Co-Gym). Recent efforts address bias in AI, societal impacts of LLMs, and ethical evaluation of human-AI systems. Awards & Honors: 2024: Sloan Research Fellowship, ONR Young Investigator Award 2023: Adamic-Glance Young Distinguished Award (ICWSM), Kavli Fellow (NAS) 2022: NSF CAREER Award, Microsoft Research Faculty Fellow 2020: IEEE AI’s 10 to Watch Advising & Grants: Dr. Yang advises over 10 PhD students and postdocs, co-leading projects on LLM evaluation (SWE-bench/SWE-smith), human-AI ethics, and culturally aware NLP. Her research is supported by NSF, Amazon, DARPA, Google, and Stanford’s HAI initiative. Labs & Teams: The SALT Lab collaborates across disciplines, with projects spanning computer science, linguistics, and social sciences. Current initiatives include developing AI tools for mental health support (AI Partner & Mentor) and auditing societal impacts of LLMs.
Benjamin Van Roy is a Professor at Stanford University since 1998, affiliated with the Departments of Electrical Engineering and Management Science and Engineering, and the Institute for Computational and Mathematical Engineering. He leads the Efficient Agent Team at Google DeepMind and previously held leadership roles at Morgan Stanley, Unica, and Enuvis. He holds SB, SM, and PhD degrees in Computer Science and Electrical Engineering from MIT, advised by John Tsitsiklis. His research focuses on reinforcement learning, alignment, and information theory, with contributions to machine learning foundations, optimization, and finance. He has authored over 150 publications, including influential works on Thompson Sampling, approximate dynamic programming, and exploration strategies. His honors include INFORMS and IEEE Fellowships and the INFORMS Lanchester Prize. Van Roy advises doctoral students across academia and industry, with graduates at top institutions and companies like Meta, Tesla, and Citadel. He teaches courses on reinforcement learning, stochastic control, and optimization. His open-source projects include Epistemic Neural Networks and the Neural Testbed for evaluating machine learning models. Key contributions include foundational work in reinforcement learning theory, scalable methods for recommendation systems, and applications in finance and resource allocation. His research bridges theoretical insights with practical applications, emphasizing alignment and safety of AI systems.
Minh Q. Phan is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering. His expertise spans system identification, iterative learning control, model predictive control, robotic swarm control, and intelligent control systems. He holds a BS from the University of California, Berkeley, and MS/M.Phil/PhD degrees from Columbia University in Mechanical Engineering. Dr. Phan has contributed to over 50 peer-reviewed publications and serves as an Associate Editor for the Journal of Guidance, Control, and Dynamics. His research focuses on advancing control theory applications in robotics, structural health monitoring, and sustainable construction materials. Key contributions include the development of OKID (Observer/Kalman Filter Identification) methods and bilinear system identification frameworks. Education History: Bachelor of Science in Mechanical Engineering, UC Berkeley, 1985 Master of Science in Mechanical Engineering, Columbia University, 1986 Master of Philosophy in Mechanical Engineering, Columbia University, 1988 Doctor of Philosophy in Mechanical Engineering, Columbia University, 1989 Research Interests: Advanced control methodologies for dynamic systems Model-based predictive control strategies Applications in robotics and aerospace engineering Structural health monitoring via system identification Machine learning for materials science Teaching Responsibilities include courses like ENGG 149 (Systems Identification), ENGS 145 (Modern Control Theory), and ENGG 148 (Structural Mechanics). His work bridges theoretical control systems with practical industrial applications, including automation in food processing and sustainable construction practices. Dr. Phan has collaborated on projects addressing viral epidemiology in Vietnam and coastal erosion mitigation strategies.
Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.
